RECOGNITION OF EMOTIONS IN BERLIN SPEECH: A HTK BASED APPROACH FOR SPEAKER AND TEXT INDEPENDENT EMOTION RECOGNITION

Abstract

Emotion recognition is one of the recent research area in speech processing to recognize the emotions from the human’s speech. It finds various applications in different fields such as, education, gaming and in call centers to improve Human machine interaction. Researchers utilized different data bases and achieved different recognition accuracies. In this paper, we have proposed HTK based emotion recognition using EMO_DB Berlin database. This contains only ten speakers, uttered each emotion 10 times and the emotions considered are Anger, Boredom, Disgust, Fear, Happy, Neutral and Sad. Speaker and text independent emotion recognition is done by using the HMM models with MFCC features, implemented by HTK. The no. of states and mixtures are varied to validate the performance of the system. This system provides recognition accuracy of 68% for HMM models with 3mimtures and 3states. The system performance is also evaluated for speaker dependent and text independent emotions which produces a recognition accuracy of 81.7%. Even with very small amount of database the system produces better accuracy which can be improved by large amount of database.